Real-time ECG Monitoring using Compressive sensing on a Heterogeneous Multicore Edge-Device

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gatewaycentric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big.littleTM multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms

Empowering Technology Enabled Care Using IoT and Smart Devices: A Review

Designing power aware wearable devices is the main key in building compact size autonomous smart devices to successfully connect health Internet of things solutions. With their ability to perform tasks ranging from simple self-monitoring to complex interactive tasks, these devices hold great promises in providing a large scale cost effective solution to the challenges facing nowadays healthcare systems. Despite the advances in sensing and hardware design, there still remain several technical challenges facing the research community to build devices that meet the computational requirements with a self-powered capability. Overcoming these challenges require major improvements in all the building blocks of wearable devices including sensors, power management, signal processing, computing architectures, and communication. This paper surveys some of the past milestones related to these subsystems and discusses promising research directions addressing their limitations. - 2001-2012 IEEE.Manuscript received October 31, 2017; revised December 13, 2017; accepted December 13, 2017. Date of publication December 22, 2017; date of current version January 31, 2018. This work was supported by National Priorities Research Program under Grant 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The associate editor coordinating the review of this paper and approving it for publication was Prof. Subhas C. Mukhopadhyay. (Corresponding author: Hamza Baali.) H. Baali was with the College of Engineering, Qatar University, Doha, Qatar. He is now with the College of Science and Engineering, Hamad bin Khalifa University, Doha, Qatar (e-mail: [email protected]).Scopu

Compressive sensing-based IoT applications: A review

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT researchScopu

IoT Based Compressive Sensing for ECG Monitoring

The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied. Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed. Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme. The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms. The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms. The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently. Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 3.3 % in the worst case scenario. 2017 IEEE.This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Joint sparsity recovery for compressive sensing based EEG system

The last decade has witnessed tremendous efforts to shape the internet of thing (IoT) platforms to be well suited for healthcare applications. These applications involve the deployment of remote monitoring platforms to collect different information about several vital signs such as electroencephalogram (EEG). However, the deployment of these platforms faces several limitations in terms of high power consumption and system complexity. High energy consumption associated with the continuous wireless data transmission can be optimized by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well chosen sensing matrices to take random projections of the data in sub-Nyquist sampling rates. In addition, system complexity can be reduced by using hardware friendly structured sensing matrices. This paper quantifies the performance of CS-based scheme for vital sign acquisition for a connected health application over an IoT platform taking multi-channel EEG signals as a case study. In addition, the paper exploits the joint sparsity of multi-channel EEG signals as well as a designed sparsifying basis to increase the sparsity of the EEG signal to improve the reconstruction quality, hence, increase the efficiency of the system. 2017 IEEE.This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

The accuracy and efficacy of real-time compressed ECG signal reconstruction on a heterogeneous multicore edge-device

Typical real-time remote health monitoring architectures consist of wearable medical devices continuously transmitting physiological signals to a nearby gateway which routes the data to an remote internet of things (IoT)-platform. Unfortunately, this model falls-short under the strict requirements of healthcare systems. Wearable medical devices have short battery lifespans, the system reliance on a cloud makes it vulnerable to connectivity and latency issues, and there are privacy concerns related to streaming sensitive medical data to remote servers. The compressive sensing (CS) scheme has been explored in the context of bio-signals to reduce the energy consumption of wearable sensors. However, CS does not address the other limitations caused by the model's reliance on cloud-computing but exacerbates the associated computing latency by requiring a computationally complex reconstruction process. In our remote elderly monitoring system, we attempt to address this weakness by developing a gateway-centric connected health system, where most signal processing and analysis occurs locally on heterogeneous multicore edge-devices. This paper explores the efficacy of real-time reconstruction of ECG signals, compressed under the CS scheme, on an IoT-gateway powered by ARM's big. LITTLE multicore solution at different signal dimension and allocated computational resources. Experimental results show the gateway's capability to reconstruct ECG signals in real-time, even when considering dimensionally large windows and minimum computational resources. Moreover, they demonstrate that utilizing more cores for the reconstruction process has a higher impact on execution time and is more energy efficient than increasing the cores' frequency. The optimal resource allocation for the majority of cases is a single big (A15) core at minimum frequency as it provides extreme fast reconstruction while consuming less or slightly more energy than its LITTLE (A7) counterpart. Heterogeneous multicore devices have the computational capacity and energy efficiency to elevate some of the limitations of a cloud-based remote health monitoring and can help create a more sustainable IoT-based connected health.Scopu

QRS Complexes Classification Using Dictionary Learning and Pietra Index

In this study, an algorithm for arrhythmia classification that conforms to the recommended practice by the Association for the Advancement of Medical Instrumentation (AAMI) is presented. Our approach efficiently exploits the inherent sparse representation of the electrocardiogram (ECG) signals. It involves, first, designing a separate dictionary for each Arrhythmia class. To this end, the alternating direction method of multipliers (ADMM) and the K-SVD, a dictionary learning algorithm based on singular value decomposition (SVD) approach, are applied. Sparse representations, based on new designed dictionaries, of each new test QRS complex are then calculated and assigned to the class associated with the largest Pietra Index (PI) afterwards. Our experiments showed promising results with accuracies ranging between 80 % and 100 %. 2017 IEEE.ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Efficient hardware implementation of the ?1-Regularized least squares for IoT edge computing

As the use of compressed sensing (CS) in internet of things (IoT) wearable nodes increases, the need for high performance and low power CS reconstruction algorithms for the battery powered IoT Edge Devices also increases. This paper describes an efficient multicore hardware implementation of the ?1 Regularized Least Squares (LS) optimization problem based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The use a decomposition technique, that exploits the special matrix structure to update its inverse, significantly reduced the processing time as well as the complexity of the algorithm. The average processing time on a parallel multicore Zynq System on Chip (SoC) device has improved by a factor of 2 compared to the C++ PC software implementation which makes it suitable for real time applications. 2017 IEEE.This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Spatial correlation aware compressed sensing for user activity detection and channel estimation in massive MTC

Abstract Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted ℓ2,1-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum a posteriori probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms

Joint estimation of clustered user activity and correlated channels with unknown covariance in mMTC

Abstract This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a clustered user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum a posteriori probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs’ clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns